

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import GLPNImageProcessor







class 					__lowercase  (						unittest.TestCase				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=7    ,						__UpperCAmelCase=3    ,						__UpperCAmelCase=18    ,						__UpperCAmelCase=30    ,						__UpperCAmelCase=4_00    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase=32    ,						__UpperCAmelCase=True    ,						)		->       Union[str, Any]:
														A      :					Any      		=					parent
														A      :					str      		=					batch_size
														A      :					Dict      		=					num_channels
														A      :					Tuple      		=					image_size
														A      :					Dict      		=					min_resolution
														A      :					Tuple      		=					max_resolution
														A      :					str      		=					do_resize
														A      :					Optional[int]      		=					size_divisor
														A      :					Tuple      		=					do_rescale






							def       snake_case			(					self						)		->       Optional[Any]:
														return {
														    "do_resize": self.do_resize,
														    "size_divisor": self.size_divisor,
														    "do_rescale": self.do_rescale,
														}







@require_torch
@require_vision
class 					__lowercase  (						_SCREAMING_SNAKE_CASE		,       unittest.TestCase				):





							"""simple docstring"""

							UpperCAmelCase_						:		str								=	GLPNImageProcessor if is_vision_available() else None




							def       snake_case			(					self						)		->       Optional[Any]:
														A      :					Optional[Any]      		=					GLPNImageProcessingTester(self						)




							@property
							def       snake_case			(					self						)		->       Union[str, Any]:
														return self.image_processor_tester.prepare_image_processor_dict()




							def       snake_case			(					self						)		->       Dict:
														A      :					str      		=					self.image_processing_class(**self.image_processor_dict						)
														self.assertTrue(hasattr(__UpperCAmelCase    ,						'''do_resize'''						)						)
														self.assertTrue(hasattr(__UpperCAmelCase    ,						'''size_divisor'''						)						)
														self.assertTrue(hasattr(__UpperCAmelCase    ,						'''resample'''						)						)
														self.assertTrue(hasattr(__UpperCAmelCase    ,						'''do_rescale'''						)						)




							def       snake_case			(					self						)		->       Dict:
														pass




							def       snake_case			(					self						)		->       Optional[Any]:
														# Initialize image_processing
														A      :					List[str]      		=					self.image_processing_class(**self.image_processor_dict						)
														# create random PIL images
														A      :					int      		=					prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=__UpperCAmelCase						)
														for image in image_inputs:
																					self.assertIsInstance(__UpperCAmelCase    ,						Image.Image						)

														# Test not batched input (GLPNImageProcessor doesn't support batching)
														A      :					int      		=					image_processing(image_inputs[0]    ,						return_tensors='''pt'''						).pixel_values
														self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
														self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)




							def       snake_case			(					self						)		->       Any:
														# Initialize image_processing
														A      :					Optional[Any]      		=					self.image_processing_class(**self.image_processor_dict						)
														# create random numpy tensors
														A      :					List[str]      		=					prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=__UpperCAmelCase    ,						numpify=__UpperCAmelCase						)
														for image in image_inputs:
																					self.assertIsInstance(__UpperCAmelCase    ,						np.ndarray						)

														# Test not batched input (GLPNImageProcessor doesn't support batching)
														A      :					str      		=					image_processing(image_inputs[0]    ,						return_tensors='''pt'''						).pixel_values
														self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
														self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)






							def       snake_case			(					self						)		->       Union[str, Any]:
														# Initialize image_processing
														A      :					int      		=					self.image_processing_class(**self.image_processor_dict						)
														# create random PyTorch tensors
														A      :					str      		=					prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=__UpperCAmelCase    ,						torchify=__UpperCAmelCase						)
														for image in image_inputs:
																					self.assertIsInstance(__UpperCAmelCase    ,						torch.Tensor						)

														# Test not batched input (GLPNImageProcessor doesn't support batching)
														A      :					Optional[int]      		=					image_processing(image_inputs[0]    ,						return_tensors='''pt'''						).pixel_values
														self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
														self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)









import math

import tensorflow as tf
from packaging import version






def        snake_case__		(    lowerCamelCase_		):
							A      :					Optional[int]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					Union[str, Any]      		=					0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0		)     ,				x.dtype		)		))

							return x * cdf






def        snake_case__		(    lowerCamelCase_		):
							A      :					List[str]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					Any      		=					tf.cast(math.pi     ,				x.dtype		)
							A      :					int      		=					tf.cast(0.044_715     ,				x.dtype		)
							A      :					Tuple      		=					0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi		) * (x + coeff * tf.pow(lowerCamelCase_     ,				3		))		))

							return x * cdf






def        snake_case__		(    lowerCamelCase_		):
							A      :					Union[str, Any]      		=					tf.convert_to_tensor(lowerCamelCase_		)

							return x * tf.tanh(tf.math.softplus(lowerCamelCase_		)		)






def        snake_case__		(    lowerCamelCase_		):
							A      :					Optional[int]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					List[Any]      		=					tf.cast(0.044_715     ,				x.dtype		)
							A      :					str      		=					tf.cast(0.7_978_845_608     ,				x.dtype		)

							return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)		))






def        snake_case__		(    lowerCamelCase_		):
							A      :					List[str]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					Any      		=					tf.cast(1.702     ,				x.dtype		)
							return x * tf.math.sigmoid(coeff * x		)






def        snake_case__		(    lowerCamelCase_		):
							return tf.clip_by_value(_gelu(lowerCamelCase_		)     ,				-10     ,				10		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_=-1		):
							A		,		A      :					Dict      		=					tf.split(lowerCamelCase_     ,				2     ,				axis=lowerCamelCase_		)
							return a * tf.math.sigmoid(lowerCamelCase_		)


if version.parse(tf.version.VERSION) >= version.parse("2.4"):






		def        snake_case__		(    lowerCamelCase_		):
									return tf.keras.activations.gelu(lowerCamelCase_     ,				approximate=lowerCamelCase_		)

		lowercase			:				Tuple   =     tf.keras.activations.gelu

		lowercase			:				Union[str, Any]   =     approximate_gelu_wrap
else:
		lowercase			:				str   =     _gelu
		lowercase			:				Dict   =     _gelu_new


lowercase			:				Union[str, Any]   =     {
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}






def        snake_case__		(    lowerCamelCase_		):
							if activation_string in ACTaFN:
														return ACTaFN[activation_string]
							else:
														raise KeyError(F'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()		)}'		)






